Humans perceive the shapes of objects quickly and reliably despite the complexity and objective ambiguities of natural images. Image features for an object are ambiguous because of the effects of projection together with large variations in occlusion, background clutter, illumination, and viewpoint. The very success of everyday vision implies neural mechanisms, yet to be understood, that organize ambiguous or "noisy" local features into objects and surfaces. Our past work in Bayesian theories of visual perception has shown how ambiguity may be resolved through the probabilistic integration of prior object knowledge with image features. We propose to extend this work to develop and test models of the functions and mechanisms underlying the resolution of ambiguity in object perception. We propose four series of experiments that use psychophysical, computational, and neuroimaging methods to investigate how the human visual system determines object shape given occlusion, constructs surface representations from ambiguous cues, estimates changes in object depth and segments shape from realistic background clutter.